Length-dependent prediction of protein intrinsic disorder |
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Authors: | Kang Peng Predrag Radivojac Slobodan Vucetic A Keith Dunker Zoran Obradovic |
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Affiliation: | (1) Center for Information Science and Technology, Temple University, Philadelphia, PA 19122, USA;(2) School of Informatics, Indiana University, Bloomington, IN 47408, USA;(3) Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA |
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Abstract: | Background Due to the functional importance of intrinsically disordered proteins or protein regions, prediction of intrinsic protein disorder from amino acid sequence has become an area of active research as witnessed in the 6th experiment on Critical Assessment of Techniques for Protein Structure Prediction (CASP6). Since the initial work by Romero et al. (Identifying disordered regions in proteins from amino acid sequences, IEEE Int. Conf. Neural Netw., 1997), our group has developed several predictors optimized for long disordered regions (>30 residues) with prediction accuracy exceeding 85%. However, these predictors are less successful on short disordered regions (≤30 residues). A probable cause is a length-dependent amino acid compositions and sequence properties of disordered regions. |
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